The fourth wave of industrialisation brought with it a digital age that meant the generation of massive amounts of data increased computing capacity and technological advancement that was unimaginable even a few decades ago.
Riding the wave, organisations quickly caught up and understood how to analyse their ever-increasing data to improve their offerings, meet dynamic market needs and cater to increasing customer expectations.
Increasingly, organisations are also leveraging AI to improve employee engagement through behaviour mapping, chatbots, real-time collaboration and feedback tools.
While analytics and data science have become a routine and expected part of a data-driven organisation, Artificial Intelligence (AI) continues to seem elusive.
Data science involves looking at a business problem and using analytical techniques to solve the problem at hand whereas AI involves making machines smarter and more human-like, using sophisticated techniques like deep learning.
Rather than using AI/ML as a passive tool, there is an opportunity to actively interact with it, allowing it to make and act on decisions on our behalf, considering its responses and changing our intentions as a result.
The onset of COVID-19 has led to a dramatic change in traditional consumer experiences. As stakeholders, customers, employees, vendors, etc. shift most aspects of their personal and professional lives onto virtual platforms, they now demand a digital-first, location-independent mindset from all digital platforms and services.
This has further spurred organisations to augment analytics with AI to build enriched customer experiences and truly transform business processes. Using AI/ML and data science together, businesses have started to reimagine the nature of the work in countless ways, some of which are listed below:
1. Persona-based propensity modelling: AI-backed data analytics enable algorithms to extract hundreds of data points from multiple data sets such as customer transaction history, location data, product preferences, etc. and map them accordingly to build well-defined customer personas.
Predictive analytics models can then prepare personalised offers, discounts, and campaigns as per a persona's preferences, and deliver them through effective channels to increase customer retention and loyalty.
2. Conversational AI: A key shift in consumer expectations due to COVID-19 is the demand for not just "always-on" digital platforms, but the associated customer services. By moving towards a hybrid help desk model, organisations can use AI to decide whether a case must be handled by a live agent or a virtual agent (for example Virtual Personal Assistants, Chatbots, etc.). Such virtual agents can be made available 24/7, interact in native languages and answer a wide variety of questions, thereby improving operational efficiencies of a help desk and revolutionising customer care.
3. Recommendation systems: AI-augmented data science techniques are also being used to deliver personalised recommendations, a marketing tool that has applications in industries ranging from media to e-commerce.
By constantly analysing customer personal data, browsing history and other metadata, companies can offer personalised recommendations to their customers in the form of individually curated content and the next best actions. This, in turn, builds a lasting relationship with the customer as well as provides the company with a desirable competitive advantage.
As the adoption of such digital tools and techniques across organisations gather steam, so does the demand for them to be utilised not just by specialists, but by all teams across the board. This democratisation of AI-enabled analytics helps organisations enable all their resources to shape their AI journey, thus opening the door to innovations, improving employee engagement and ultimately, enhancing customer experience.
Automated Machine Learning or AutoML, for example, can be used to automate the entire model development lifecycle.
This means that any non-technical professional from functions such as finance, HR, procurement, etc. can upload their data to the AutoML platform, generate hundreds of models based on the existing ML algorithms, pick the best performing model that suits their ask and use the insights generated from that model to guide their business decisions.
Similarly, various cloud technology providers such as Microsoft, Amazon and Google have released premade, drag-and-drop or no-code AI tools that allow professionals to integrate AI into applications without needing the technical know-how.
Organisations are now making themselves future-ready by blending AI and data science to deliver impactful and game-changing research.
Several utility companies, for instance, are exploring remote sensing, cloud, data analytics, and AI to fundamentally transform how infrastructure is managed.
The future of infrastructure management will be delivered by robots in the field, data analytics in the cloud, and AI embedded in the process. Such amalgamation can provide unique insights, which when incorporated into an organisation's overall business strategy and objectives can have a swift and measurable impact on the business.
(Saurabh Kumar, Partner and Vishesh Tewari, Director, Deloitte India.)
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